比較するには長さが一致している必要があります（2つの基準でのパンダの選択）

私は次のようにユーザーごとに値を生成しています：

loDf = locDfs[user] # locDfs is a copy of locationDf elsewhere in the code... sorry for all the variable names.
loDf.reset_index(inplace=True)
loDf = pd.crosstab([loDf.date, loDf.uid], loDf.location_id)
loDf.reset_index(inplace=True)

loDf.set_index('date', inplace=True)
loDf.drop('uid', axis=1, inplace=True)

# join the location crosstab columns with the app crosstab columns per user
userLocAppDfs[user] = userLocAppDfs[user].join(loDf, how='outer')
# convert from just "1" at each location change event followed by zeros, to "1" continuing until next location change
userLocAppDfs[user] = userLocAppDfs[user].resample('15T').agg('max')
userLocAppDfs[user]['uid'].fillna(user, inplace=True)

これにより、位置データが取得され、location_idが列に変換され、時系列で他のデータと結合されます。

それは本質的にデータの再形成をカバーしています。次に、正規化する必要があります。これを行うには、各列の値を確認する必要があります。

for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():

完全な機能は次のとおりです。

def normalize(inputMetricDf, inputLocationDf):
    '''
    normalize, resample, and combine data into a single data source
    '''
    metricDf = inputMetricDf.copy()
    locationDf = inputLocationDf.copy()

    appDf = metricDf[['date', 'uid', 'app_id', 'metric']].copy()

    locDf = locationDf[['date', 'uid', 'location_id']]
    locDf.set_index('date', inplace=True)

    # convert location data to "15 minute interval" rows
    locDfs = {}
    for user, user_loc_dc in locDf.groupby('uid'):
        locDfs[user] = user_loc_dc.resample('15T').agg('max').bfill()

    aDf = appDf.copy()
    aDf.set_index('date', inplace=True)

    userLocAppDfs = {}
    user = ''
    for uid, a2_df in aDf.groupby('uid'):
        user = uid
        # per user, convert app data to 15m interval
        userDf = a2_df.resample('15T').agg('max')

        # assign metric for each app to an app column for each app, per user
        userDf.reset_index(inplace=True)
        userDf = pd.crosstab(index=userDf['date'], columns=userDf['app_id'],
                             values=userDf['metric'], aggfunc=np.mean).fillna(np.nan, downcast='infer')

        userDf['uid'] = user

        userDf.reset_index(inplace=True)
        userDf.set_index('date', inplace=True)

        # reapply 15m intervals now that we have new data per app
        userLocAppDfs[user] = userDf.resample('15T').agg('max')

        # assign location data to location columns per location, creates a "1" at the 15m interval of the location change event in the location column created
        loDf = locDfs[user]
        loDf.reset_index(inplace=True)
        loDf = pd.crosstab([loDf.date, loDf.uid], loDf.location_id)
        loDf.reset_index(inplace=True)

        loDf.set_index('date', inplace=True)
        loDf.drop('uid', axis=1, inplace=True)

        # join the location crosstab columns with the app crosstab columns per user
        userLocAppDfs[user] = userLocAppDfs[user].join(loDf, how='outer')
        # convert from just "1" at each location change event followed by zeros, to "1" continuing until next location change
        userLocAppDfs[user] = userLocAppDfs[user].resample('15T').agg('max')
        userLocAppDfs[user]['uid'].fillna(user, inplace=True)

        for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():
            # fill location NaNs
            userLocAppDfs[user][loc] = userLocAppDfs[user][loc].replace(
                np.nan, 0)

        # fill app NaNs
        for app in a2_df['app_id'].unique():
            userLocAppDfs[user][app].interpolate(
                method='linear', limit_area='inside', inplace=True)
            userLocAppDfs[user][app].fillna(value=0, inplace=True)

    df = userLocAppDfs[user].copy()

    # ensure actual normality
    alpha = 0.05
    for app in aDf['app_id'].unique():
        _, p = normaltest(userLocAppDfs[user][app])
        if(p > alpha):
            raise DataNotNormal(args=(user, app))

    # for loc in userLocAppDfs[user]:
        # could also test location data

    return df

しかし、これはエラーを生成します：

  File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 346, in run_http_function
    result = _function_handler.invoke_user_function(flask.request)
  File "/env/local/lib/python3.7/site-packages/google/cloud/functions/worker.py", line 223, in invoke_user_function
    loop.run_until_complete(future)
  File "/opt/python3.7/lib/python3.7/asyncio/base_events.py", line 573, in run_until_complete
    return future.result()
  File "/user_code/main.py", line 31, in default_model
    train, endog, exog, _, _, rawDf = preprocess(ledger, apps)
  File "/user_code/Wrangling.py", line 67, in preprocess
    rawDf = normalize(appDf, locDf)
  File "/user_code/Wrangling.py", line 185, in normalize
    for loc in locationDf[(locationDf['location_id'] in loDf.columns.values) & (locationDf['uid'] == user)].location_id.unique():
  File "/env/local/lib/python3.7/site-packages/pandas/core/ops.py", line 1745, in wrapper
    raise ValueError('Lengths must match to compare')
ValueError: Lengths must match to compare

形を変えたためにlocationsDfから場所が失われる可能性があることに気付く前は、次のことを行っていました。

    for loc in locationDf[locationDf['uid'] == user].location_id.unique():

そしてこれは働いていた他のすべてのインスタンスに対して。しかし、同じ15tタイムセグメントに2つの場所があり、そのうちの1つがそこにしか表示されないが、15tウィンドウのためにドロップされた場合、エラーが発生します。だから私は他の条件が必要です。

locationDf ['location_id']は、クロス集計の列名と同じように、単なる文字列です。

なぜこれが私にエラーを投げるのですか？

試行された回答のエラー：

    for loc in locationDf[(locationDf['location_id'].isin(loDf.columns.values)) & (locationDf['uid'].isin([user])), 'location_id'].unique():
  File "/env/local/lib/python3.7/site-packages/pandas/core/frame.py", line 2927, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/env/local/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2657, in get_loc
    return self._engine.get_loc(key)
  File "pandas/_libs/index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 110, in pandas._libs.index.IndexEngine.get_loc
TypeError: '(0       True
1       True
2       True
3      False
4       True
5       True
6      False
7       True
8       True
9      False
10     False
11     False
12     False
13     False
14     False
15     False
16     False
17     False
18     False
19      True
20      True
21      True
22      True
23      True
24      True
25      True
26     False
27      True
28      True
29     False
       ...  
210    False
211    False
212    False
213    False
214    False
215    False
216    False
217    False
218     True
219     True
220    False
221    False
222    False
223    False
224    False
225    False
226     True
227    False
228     True
229    False
230    False
231     True
232    False
233     True
234    False
235    False
236    False
237     True
238    False
239    False
Length: 240, dtype: bool, 'location_id')' is an invalid key